Metadata-Version: 2.1
Name: LensFlare
Version: 0.0.1
Summary: A small library of hand-rolled deep learning models
Home-page: UNKNOWN
Author: Gordon MacMillan
Author-email: gmacilla@ymail.com
License: UNKNOWN
Description: 
        # LensFlare
        
        LensFlare is an example package I created to help myself and others better understand neural networks. A lot of the code is based off work that I did in the [Coursera deeplearning.ai course](https://www.coursera.org/specializations/deep-learning)
        
        An example work flow is shown below:
        
        
        ```python
        import tensorflow as tf
        from lensflare.classification import TfNNClassifier
        from lensflare.util import load_moons_dataset
        ```
        
        
        ```python
        X_train, y_train = load_moons_dataset()
        ```
        
        
        ![png](lensflare_api_example_files/lensflare_api_example_2_0.png)
        
        
        
        ```python
        tf.reset_default_graph()
        
        # layer_dims contains neural network structure parameters
        layers_dims=[X_train.shape[0], 200, 80, 10, 1]
        clf = TfNNClassifier(layers_dims=layers_dims,
                          optimizer="adam",
                          lambd=.05,
                          keep_prob=0.7,
                          num_epochs=5000)
        clf.fit(X_train, y_train, seed=3)
        y_pred_train = clf.transform(X_train, y_train)
        ```
        
            Cost after epoch 0: 1.036825
            Cost after epoch 1000: 0.108737
            Cost after epoch 2000: 0.104837
            Cost after epoch 3000: 0.106805
            Cost after epoch 4000: 0.105311
            INFO:tensorflow:Restoring parameters from results/model
            Training Accuracy: 0.983333333333
        
        
        
        ```python
        from lensflare.funcs.tf_funcs import plot_decision_boundary, predict_dec
        # Plot decision boundary
        
        predictions, X, dropout_var, sess = predict_dec()
        model = lambda X_train: sess.run([predictions], feed_dict={X:X_train, dropout_var: 1.0});
        
        plot_decision_boundary(model, X_train, y_train)
        sess.close()
        ```
        
            INFO:tensorflow:Restoring parameters from results/model
        
        
        
        ![png](lensflare_api_example_files/lensflare_api_example_4_1.png)
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
